[SOLVED] Cse343/ece343 assignment 4- cnn, pca, k-means clustering
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• Your submission should be a single zip file 2020xxx_HW1.zip (Where 2020xxx is your roll number). Include all the files (code and report with theory questions) arranged with proper names. A single .pdf report explaining your codes with results, relevant graphs, visualization and solution to theory questions should be there. The structure of submission should follow:
2020xxx_HW1
|− code_rollno.py/.ipynb
|− report_rollno.pdf
|− (All other files for submission)
• Anything not in the report will not be graded.
• Your code should be neat and well-commented.
• You have to do either Section B or C.
• Section A is mandatory.1. (10 points) Section A (Theoretical)
The kernels are of shape h × w × I × O, representing height, width, number of input channels, and number of output channels, respectively.
(a) What is the output image size? [2]
(b) What is the significance of pooling in CNN? [1](c) Compute the total number of learnable parameters for the above CNN architecture (ignore bias) [2]
(d) (1 mark) Explain the difference between linear and non-linear kernel or filters in CNN.
2. (15 points) Section B (Scratch Implementation)
(a) For this problem, you have to implement Convolutional neural network from scratch. To implement the CNN from scratch you have to implement the following required functions by yourself: You are allowed to use NumPy, Matplotlib, and random libraries.
OR
3. (15 points) Section C (Algorithm implementation using packages) Clustering Analysis using PCA and K-Means
Dataset: Country Dataset
The task is to perform a clustering analysis to categorize countries based on socioeconomic and health factors. You are provided with a dataset containing socio-economic
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and health indicators for various countries. The columns include features such as child mortality, exports, health spending, imports, income, inflation, life expectancy, total fertility rate, and GDP per capita.
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